An ultrasound imaging system provides useful information about the interior characteristics of an object or subject under examination. Ultrasound B-mode images have included grayscale information related to the magnitude of the reflected acoustic signal at each point imaged. Different forms (e.g., tissue elasticity, sound speed, optical absorption, spatial coherence, etc.) of image contrast provide additional useful information.
The quality of an ultrasound image depends on many factors. Examples of such factors include, but are not limited to, the physics of acoustic propagation, reflection, and diffraction phenomena. The quality of an ultrasound image is also impacted by the approaches by which the acoustic echoes are sampled by the front end analog/digital hardware. The quality of an ultrasound image is also impacted by the approaches by which the sampled acoustic echoes are subsequently processed by the beamformer.
The literature has focused on beamforming strategies. These strategies have ranged from passive techniques (e.g. apodization, spatial compounding, frequency compounding, coded excitation, etc.) to data adaptive techniques (e.g., minimum variance distortionless response, Capon, coherent processing, phase coherence processing, spatial coherence processing, etc.). However, most, if not all, of these techniques offer a tradeoff between resolution, contrast, signal-noise-ratio (SNR), computational complexity, and/or artifact suppression.
A trend in medical ultrasound has been the advancement of real-time 3D imaging and the application of a large transducer element count (e.g., greater than one thousand (1000) transducer elements) two-dimensional (2D) phased array. Unfortunately, the processing demands, as well as hardware complexities, consumed to utilize such a large number of transducer elements in an array places even further constraints on the processing strategies employed during data processing.